Distinguishing enzymes using metabolome data for the hybrid dynamic/static method
<p>Abstract</p> <p>Background</p> <p>In the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of t...
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doaj-c841fa3ddbe143fea9bc26744b9944412020-11-25T00:42:23ZengBMCTheoretical Biology and Medical Modelling1742-46822007-05-01411910.1186/1742-4682-4-19Distinguishing enzymes using metabolome data for the hybrid dynamic/static methodNakayama YoichiIshii NobuyoshiTomita Masaru<p>Abstract</p> <p>Background</p> <p>In the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of these parameters is time-consuming. Therefore, for large-scale modelling, it is essential to develop a method that requires few experimental parameters. The hybrid dynamic/static (HDS) method is a combination of the conventional kinetic representation and metabolic flux analysis (MFA). Since no kinetic information is required in the static module, which consists of MFA, the HDS method may dramatically reduce the number of required parameters. However, no adequate method for developing a hybrid model from experimental data has been proposed.</p> <p>Results</p> <p>In this study, we develop a method for constructing hybrid models based on metabolome data. The method discriminates enzymes into static modules and dynamic modules using metabolite concentration time series data. Enzyme reaction rate time series were estimated from the metabolite concentration time series data and used to distinguish enzymes optimally for the dynamic and static modules. The method was applied to build hybrid models of two microbial central-carbon metabolism systems using simulation results from their dynamic models.</p> <p>Conclusion</p> <p>A protocol to build a hybrid model using metabolome data and a minimal number of kinetic parameters has been developed. The proposed method was successfully applied to the strictly regulated central-carbon metabolism system, demonstrating the practical use of the HDS method, which is designed for computer modelling of metabolic systems.</p> http://www.tbiomed.com/content/4/1/19 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Nakayama Yoichi Ishii Nobuyoshi Tomita Masaru |
spellingShingle |
Nakayama Yoichi Ishii Nobuyoshi Tomita Masaru Distinguishing enzymes using metabolome data for the hybrid dynamic/static method Theoretical Biology and Medical Modelling |
author_facet |
Nakayama Yoichi Ishii Nobuyoshi Tomita Masaru |
author_sort |
Nakayama Yoichi |
title |
Distinguishing enzymes using metabolome data for the hybrid dynamic/static method |
title_short |
Distinguishing enzymes using metabolome data for the hybrid dynamic/static method |
title_full |
Distinguishing enzymes using metabolome data for the hybrid dynamic/static method |
title_fullStr |
Distinguishing enzymes using metabolome data for the hybrid dynamic/static method |
title_full_unstemmed |
Distinguishing enzymes using metabolome data for the hybrid dynamic/static method |
title_sort |
distinguishing enzymes using metabolome data for the hybrid dynamic/static method |
publisher |
BMC |
series |
Theoretical Biology and Medical Modelling |
issn |
1742-4682 |
publishDate |
2007-05-01 |
description |
<p>Abstract</p> <p>Background</p> <p>In the process of constructing a dynamic model of a metabolic pathway, a large number of parameters such as kinetic constants and initial metabolite concentrations are required. However, in many cases, experimental determination of these parameters is time-consuming. Therefore, for large-scale modelling, it is essential to develop a method that requires few experimental parameters. The hybrid dynamic/static (HDS) method is a combination of the conventional kinetic representation and metabolic flux analysis (MFA). Since no kinetic information is required in the static module, which consists of MFA, the HDS method may dramatically reduce the number of required parameters. However, no adequate method for developing a hybrid model from experimental data has been proposed.</p> <p>Results</p> <p>In this study, we develop a method for constructing hybrid models based on metabolome data. The method discriminates enzymes into static modules and dynamic modules using metabolite concentration time series data. Enzyme reaction rate time series were estimated from the metabolite concentration time series data and used to distinguish enzymes optimally for the dynamic and static modules. The method was applied to build hybrid models of two microbial central-carbon metabolism systems using simulation results from their dynamic models.</p> <p>Conclusion</p> <p>A protocol to build a hybrid model using metabolome data and a minimal number of kinetic parameters has been developed. The proposed method was successfully applied to the strictly regulated central-carbon metabolism system, demonstrating the practical use of the HDS method, which is designed for computer modelling of metabolic systems.</p> |
url |
http://www.tbiomed.com/content/4/1/19 |
work_keys_str_mv |
AT nakayamayoichi distinguishingenzymesusingmetabolomedataforthehybriddynamicstaticmethod AT ishiinobuyoshi distinguishingenzymesusingmetabolomedataforthehybriddynamicstaticmethod AT tomitamasaru distinguishingenzymesusingmetabolomedataforthehybriddynamicstaticmethod |
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1725282899236749312 |